Build Generative AI Apps and Agents in the Cloud

A generative AI application uses a large language model, an AI trained on large amounts of text, to write, summarize or answer questions in everyday language. For business use, it connects the model to your own data and systems, with guardrails, logging, cost tracking and a way to test answers.

An AI agent is an application that can take actions as well as answer questions, such as looking up an order, updating a record or raising a ticket. Agents need clear limits and a person’s approval for anything important.

Beyond the model, you need access to the right data, guardrails, security, logging, cost tracking, a set of test questions and someone who owns the application. These turn a demo into something people can rely on every day.

Microsoft’s data privacy page for Azure OpenAI states that prompts and responses (what you send and what the model sends back) are not available to OpenAI or other customers and are not used to train foundation models without your permission. AWS’s Amazon Bedrock data protection guide states that model providers do not have access to customer prompts and responses. Some data can still be kept for a time: on Bedrock, certain models keep traffic for up to 30 days for abuse detection, and Azure abuse monitoring can store flagged prompts and responses for review.

Amazon Bedrock offers models from several providers, including Anthropic’s Claude, and suits teams whose data and applications run on AWS. Azure OpenAI suits businesses invested in Microsoft 365 and Azure. The cloud where your data already sits is a strong deciding factor.

On Azure OpenAI, prompts and responses are processed in the geography you choose, unless you choose a Global or Data Zone setup, which shares processing across regions. On Amazon Bedrock, requests run in the AWS Region you choose unless cross-region inference is switched on, which can process them in other Regions. We help you pick a setup that matches your data rules.

Ground each answer in approved company documents and data, show the source and test the application against real questions with known answers before each release. Users can then check an answer when it matters.

Guardrails are filters and limits around the model. They help block harmful content, keep sensitive data out of answers and stop the application from going beyond its task. Azure OpenAI checks requests and replies against its guardrails, previously called content filters, as they are processed.

Cost depends on the model, the length of each request and how many people use it. We estimate monthly cost before launch, match model size to each task and track spend per application so cost stays visible.

Use the smallest model that gives good answers for the task. Simple tasks, such as sorting requests, can use a smaller, cheaper model, while complex writing or reasoning may need a larger one. We test options against your questions.

Give each agent only the access it needs, set clear limits on what it may change, require a person’s approval for important actions and log every action for review.

For many business tasks, giving the model your documents and data at the time of each question is enough. Fine-tuning, which means training the model further on your own examples, helps when a task needs a particular style or format that prompts alone do not achieve.

Collect real questions with correct answers and run them before each release. In use, track how users rate answers, error rates, response times and cost per request.

Staff are more likely to switch when an approved tool is easy to use and approved for work data. An approved application in your cloud, with clear rules, gives them that option. Our Govern and Secure Enterprise AI Use use case covers the policy side.

AI knowledge search answers questions from your documents. This page covers building any generative AI application or agent, including ones that draft content, follow processes and act on your systems. Our Find Answers Across Business Documents with AI use case covers document search.

A business owner decides what the application is for and whether its answers are good enough. An AI lead or IT owner runs it, watches cost and handles changes to models and data.

We work with AWS, Microsoft and Claude as a Technology Partner, building generative AI applications on Amazon Bedrock and Azure OpenAI.

The Generative AI Review looks at your AI ideas, pilots, data, systems and security needs, and ranks use cases by value and risk. It needs your list of ideas or pilots, access to sample documents and data and time with business and IT owners. We walk you through the findings on a call. Signisys is based in Noida and works with businesses in Delhi NCR and across India; book through this page or call +91 7669 966 966. A Signisys specialist responds within 1 business day, and there is no obligation to go further.